Prediction of freezing of gait in patients with Parkinson's disease using EEG signals

A.M.A. Handojoseno; G.R. Naik; M. Gilat; J.M. Shine; T.N. Nguyen; T.L.Y. Quynh; S.J.G. Lewis; H.T. Nguyen

doi:10.3233/978-1-61499-845-7-124

Prediction of freezing of gait in patients with Parkinson's disease using EEG signals

A.M.A. Handojoseno, G.R. Naik, M. Gilat, J.M. Shine, T.N. Nguyen, T.L.Y. Quynh, S.J.G. Lewis, H.T. Nguyen

Western Sydney University

Research output: Chapter in Book / Conference Paper › Chapter

30 Citations (Scopus)

Abstract

Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson’s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.

Original language	English
Title of host publication	Telehealth for our Ageing Society: Selected Papers from Global Telehealth 2017
Editors	Maayken E. van den Berg, Anthony J. Maeder
Place of Publication	Netherlands
Publisher	IOS Press
Pages	124-131
Number of pages	8
ISBN (Electronic)	9781614998457
ISBN (Print)	9781614998440
DOIs	https://doi.org/10.3233/978-1-61499-845-7-124
Publication status	Published - 2018

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10.3233/978-1-61499-845-7-124

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Handojoseno, A. M. A., Naik, G. R., Gilat, M., Shine, J. M., Nguyen, T. N., Quynh, T. L. Y., Lewis, S. J. G., & Nguyen, H. T. (2018). Prediction of freezing of gait in patients with Parkinson's disease using EEG signals. In M. E. van den Berg, & A. J. Maeder (Eds.), Telehealth for our Ageing Society: Selected Papers from Global Telehealth 2017 (pp. 124-131). IOS Press. https://doi.org/10.3233/978-1-61499-845-7-124

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abstract = "Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson{\textquoteright}s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.",

author = "A.M.A. Handojoseno and G.R. Naik and M. Gilat and J.M. Shine and T.N. Nguyen and T.L.Y. Quynh and S.J.G. Lewis and H.T. Nguyen",

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Handojoseno, AMA, Naik, GR, Gilat, M, Shine, JM, Nguyen, TN, Quynh, TLY, Lewis, SJG & Nguyen, HT 2018, Prediction of freezing of gait in patients with Parkinson's disease using EEG signals. in ME van den Berg & AJ Maeder (eds), Telehealth for our Ageing Society: Selected Papers from Global Telehealth 2017. IOS Press, Netherlands, pp. 124-131. https://doi.org/10.3233/978-1-61499-845-7-124

Prediction of freezing of gait in patients with Parkinson's disease using EEG signals. / Handojoseno, A.M.A.; Naik, G.R.; Gilat, M. et al.
Telehealth for our Ageing Society: Selected Papers from Global Telehealth 2017. ed. / Maayken E. van den Berg; Anthony J. Maeder. Netherlands: IOS Press, 2018. p. 124-131.

Research output: Chapter in Book / Conference Paper › Chapter

TY - CHAP

T1 - Prediction of freezing of gait in patients with Parkinson's disease using EEG signals

AU - Handojoseno, A.M.A.

AU - Naik, G.R.

AU - Gilat, M.

AU - Shine, J.M.

AU - Nguyen, T.N.

AU - Quynh, T.L.Y.

AU - Lewis, S.J.G.

AU - Nguyen, H.T.

PY - 2018

Y1 - 2018

N2 - Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson’s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.

AB - Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson’s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.

UR - https://hdl.handle.net/1959.7/uws:66968

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DO - 10.3233/978-1-61499-845-7-124

M3 - Chapter

SN - 9781614998440

SP - 124

EP - 131

BT - Telehealth for our Ageing Society: Selected Papers from Global Telehealth 2017

A2 - van den Berg, Maayken E.

A2 - Maeder, Anthony J.

PB - IOS Press

CY - Netherlands

ER -

Prediction of freezing of gait in patients with Parkinson's disease using EEG signals

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